The past few decades have witnessed the tremendous progress of human-machine interface (HMI) in communication, education, and manufacturing fields. However, due to signal acquisition devices' limitations, the research on HMI related to communication aid applications for the disabled is progressing slowly. Here, inspired by frogs' croaking behavior, a bionic triboelectric nanogenerator (TENG)-based ultra-sensitive self-powered electromechanical sensor for muscle-triggered communication HMI application is developed. The sensor possesses a high sensitivity (54.6 mV mm-1 ), a high-intensity signal (± 700 mV), and a wide sensing range (0-5 mm). The signal intensity is 206 times higher than that of traditional biopotential electromyography methods. By leveraging machine learning algorithms and Morse code, the safe, accurate (96.3%), and stable communication aid HMI applications are achieved. The authors' bionic TENG-based electromechanical sensor provides a valuable toolkit for HMI applications of the disabled, and it brings new insights into the interdisciplinary cross-integration between TENG technology and bionics.
Keywords: Morse code; bionics; human-machine interfaces; machine learning; triboelectric nanogenerators.
© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.